Exploring the association between the Healthy Eating Food Index-2019 (HEFI-2019), the Canadian Healthy Eating Index 2007 (C-HEI 2007), and health among First Nations adults across Canada
Bibliographic record
Abstract
Assessing diet quality is crucial in public health research, as it reveals relationships between dietary patterns and health. The Healthy Eating Food Index-2019 (HEFI-2019) and the Canadian Healthy Eating Index 2007 (C-HEI 2007) are robust tools used to evaluate adherence to dietary guidelines. While the C-HEI 2007 has been widely applied in nutritional epidemiology, research exploring associations between the HEFI-2019, which reflects the updated 2019 Canadian dietary guidelines, and health remains limited. Given the distinct dietary habits and health profiles of First Nations, evaluating these indices in this population is essential. This study investigates associations between HEFI-2019 and C-HEI 2007 scores and health variables, including obesity, type 2 diabetes (T2D), and self-perceived health among First Nations adults. Data were drawn from the First Nations Food, Nutrition and Environment Study, which included interviews and 24 h dietary recalls from 5455 adults across 92 communities. Higher HEFI-2019 scores were significantly associated with increased odds of T2D but not with obesity or self-perceived health. C-HEI 2007 scores were also associated with T2D, with stronger associations in the highest tertile. However, C-HEI 2007 scores were not significantly associated with obesity or self-perceived health. The study highlights the significant relationships between HEFI-2019 and C-HEI 2007 scores and T2D among First Nations adults, underscoring the role of diet quality in chronic disease management. The positive associations with T2D may reflect dietary improvements following diagnosis, wherein individuals adopt healthier eating habits. The absence of associations between these indices and obesity or self-perceived health may also be explained by reverse causation, potentially obscuring expected associations. Furthermore, factors such as socioeconomic status and access to healthcare likely contribute to these outcomes. However, given the cross-sectional design, causal relationships cannot be established, and the observed associations should be interpreted with caution. These findings underscore the need for culturally relevant dietary interventions to improve health in Indigenous populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".